docs: def _compute_anom_data_decay_all(self):
          """
          Compute anomaly scores using a lagging window covering all the data points before.
          """
          anom_scores = {}
          values = self.time_series.values
          ema = utils.compute_ema(self.smoothing_factor, values)
          stdev = numpy.std(values)
          for i, (timestamp, value) in enumerate(self.time_series_items):
              anom_score = abs((value - ema[i]) / stdev) if stdev else value - ema[i]
              anom_scores[timestamp] = anom_score
          self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))